In order to scale up BO to high-dimensional parameter space, repertoire-based learning in robotics performs the policy optimization on the low dimensional outcome-space. The core idea behind this approach is to first pre-compute a large and diverse set of policies in simulation with a “quality-diversity” algorithm [1]}, [2]} and associate them with unique low-dimensional discrete outcomes. Then an optimization process (e.g., BO) figures out the policy on that discrete outcome space that works best in current dynamics conditions [3]}, [4]}, [5]}, [6]}. For instance, IT&E approach  [4]} evolves a policy repertoire for a Hexapod robot to walk forward in simulation, but with different walking gaits (here gaits are the outcomes). On the physical robot with a high reality-gap due to a damaged leg, IT&E performs BO to figure out the gait (and so the associated policy in the repertoire) that makes the robot walk forward.
